Face Recognition Algorithm Using the Discrete Gabor Transform

Aguilar-Torres, Gualberto; Sánchez-Pérez, Gabriel; Nakano-Miyatake, Mariko

doi:10.1109/conielecomp.2007.18

Cited by 5 publications

(2 citation statements)

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“…Section 1 mentioned results of research regarding image pattern recognition. Wavelet transform has been used for feature selection and NN classification (Fuentes G, 2008); results were compared to other proposed feature selection methods concerning faces (Aguilar et al, 2007) (Table 2).…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

Using genetic algorithm feature selection in neural classification systems for image pattern recognition

Gamarra¹,

Quintero²

2013

Ing. Inv.

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Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbour classifier. The proposed approach performed better than the other methods.

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Section: Comparison With Other Approachesmentioning

confidence: 99%

Using genetic algorithm feature selection in neural classification systems for image pattern recognition

Gamarra¹,

Quintero²

2013

Ing. Inv.

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“…Among these method are the di↵erent Gabor Transform approaches 7,8 and di↵erent variations of Local Binary Pattern (LBP) 9, 10 algorithms, including an LBP-derived method known as Local Ternary Pattern (LTP) 11 . LBP generates an histogram of pixel-level texture features of the image, and uses it as a facial descriptor.…”

Section: Introductionmentioning

confidence: 99%

An embedded face-classification system for infrared images on an FPGA

Soto

Figueroa

2014

SPIE Proceedings

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We present a face-classification architecture for long-wave infrared (IR) images implemented on a Field Programmable Gate Array (FPGA). The circuit is fast, compact and low power, can recognize faces in real time and be embedded in a larger image-processing and computer vision system operating locally on an IR camera. The algorithm uses Local Binary Patterns (LBP) to perform feature extraction on each IR image. First, each pixel in the image is represented as an LBP pattern that encodes the similarity between the pixel and its neighbors. Uniform LBP codes are then used to reduce the number of patterns to 59 while preserving more than 90% of the information contained in the original LBP representation. Then, the image is divided into 64 non-overlapping regions, and each region is represented as a 59-bin histogram of patterns. Finally, the algorithm concatenates all 64 regions to create a 3,776-bin spatially enhanced histogram. We reduce the dimensionality of this histogram using Linear Discriminant Analysis (LDA), which improves clustering and enables us to store an entire database of 53 subjects on-chip. During classification, the circuit applies LBP and LDA to each incoming IR image in real time, and compares the resulting feature vector to each pattern stored in the local database using the Manhattan distance. We implemented the circuit on a Xilinx Artix-7 XC7A100T FPGA and tested it with the UCHThermalFace database, which consists of 28 81 ⇥ 150-pixel images of 53 subjects in indoor and outdoor conditions. The circuit achieves a 98.6% hit ratio, trained with 16 images and tested with 12 images of each subject in the database. Using a 100 MHz clock, the circuit classifies 8,230 images per second, and consumes only 309mW.

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